headlines_df <- read_csv("Fake.csv")
## Rows: 23481 Columns: 4
## ── Column specification ──────────────────────────────────────────────────────────
## Delimiter: ","
## chr (4): title, text, subject, date
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
headlines_df$title <- tolower(headlines_df$title)
tidy_headlines <- headlines_df %>%
unnest_tokens(word, title)
tidy_headlines <- tidy_headlines %>%
anti_join(stop_words)
## Joining with `by = join_by(word)`
tidy_headlines <- tidy_headlines %>%
filter(nchar(word) > 1)
tidy_headlines$word <- gsub("[[:punct:]]", "", tidy_headlines$word)
word_freq <- tidy_headlines %>%
count(word, sort = TRUE)
head(word_freq, 10)
## # A tibble: 10 × 2
## word n
## <chr> <int>
## 1 video 8477
## 2 trump 7874
## 3 watch 1935
## 4 obama 1901
## 5 hillary 1896
## 6 trumps 1475
## 7 president 1141
## 8 clinton 1001
## 9 breaking 896
## 10 black 894
bigrams <- headlines_df %>%
unnest_tokens(bigram, title, token = "ngrams", n = 2) %>%
count(bigram, sort = TRUE)
head(bigrams, 10)
## # A tibble: 10 × 2
## bigram n
## <chr> <int>
## 1 donald trump 710
## 2 president trump 428
## 3 of the 373
## 4 in the 352
## 5 white house 345
## 6 hillary clinton 343
## 7 trump is 318
## 8 fox news 314
## 9 on the 296
## 10 to be 295
trigrams <- headlines_df %>%
unnest_tokens(trigram, title, token = "ngrams", n = 3) %>%
count(trigram, sort = TRUE)
head(trigrams, 10)
## # A tibble: 10 × 2
## trigram n
## <chr> <int>
## 1 black lives matter 160
## 2 boiler room ep 138
## 3 the white house 92
## 4 you won’t believe 75
## 5 on social media 53
## 6 new york times 51
## 7 to vote for 49
## 8 in front of 48
## 9 donald trump jr 47
## 10 supreme court justice 47
quadgrams <- headlines_df %>%
unnest_tokens(quadgram, title, token = "ngrams", n = 4) %>%
count(quadgram, sort = TRUE)
head(quadgrams, 10)
## # A tibble: 10 × 2
## quadgram n
## <chr> <int>
## 1 a handed to him 28
## 2 his a handed to 26
## 3 to go f ck 26
## 4 gets his a handed 24
## 5 make america great again 23
## 6 handed to him by 21
## 7 black lives matter terrorists 20
## 8 go f ck himself 20
## 9 want you to see 20
## 10 watch what happens when 20
pentagrams <- headlines_df %>%
unnest_tokens(pentagram, title, token = "ngrams", n = 5) %>%
count(pentagram, sort = TRUE)
head(pentagrams, 10)
## # A tibble: 10 × 2
## pentagram n
## <chr> <int>
## 1 his a handed to him 26
## 2 gets his a handed to 24
## 3 <NA> 22
## 4 to go f ck himself 20
## 5 a handed to him by 18
## 6 to shut down free speech 15
## 7 you need to know about 15
## 8 patrick henningsen live with guest 12
## 9 trump to go f ck 12
## 10 trump gets his a handed 10
hexagrams <- headlines_df %>%
unnest_tokens(hexagram, title, token = "ngrams", n = 6) %>%
count(hexagram, sort = TRUE)
head(hexagrams, 10)
## # A tibble: 10 × 2
## hexagram n
## <chr> <int>
## 1 <NA> 80
## 2 gets his a handed to him 24
## 3 his a handed to him by 16
## 4 trump to go f ck himself 12
## 5 trump gets his a handed to 10
## 6 everything you need to know about 7
## 7 to shut down free speech video 7
## 8 before vince foster was found dead 6
## 9 bill clinton fired his fbi director 6
## 10 climate change is part of obamatrade 6
set.seed(123)
wordcloud(words = word_freq$word,
freq = word_freq$n,
min.freq = 2,
max.words = 100,
random.order = FALSE,
rot.per = 0.3,
colors = brewer.pal(8, "Dark2"))
top_words <- word_freq %>% slice_max(n, n = 15)
ggplot(top_words, aes(x = reorder(word, n), y = n)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = "Top Words in Fake News Headlines",
x = "Words", y = "Frequency") +
theme_minimal()
# ‘Video’ and ‘Trump’ are the two most common words in the fake news
headlines dataset, both words occurring at least four times as much as
other common words. ‘Video’ is mostly used at the beginning or end of
headlines, denoting that the article contains a video (Eg - While
Honoring Native American Code Talkers, Trump Called Elizabeth Warren
‘Pocahontas’ (VIDEO)). ‘Tweets’ is another word used in the same way -
to denote the content type and source of the news - but this occurs ten
times less than ‘video’, suggesting visual elements are ten times more
common than tweets for fake news. ‘Watch’ is the third most common word,
this is used as a substitute for ‘video’ (Eg - WATCH: Nicolle Wallace
Takes Trump To The Woodshed For Backing Pedophile Moore).
bigrams %>%
filter(n > 5) %>%
wordcloud2(size = 0.5)
top_bigrams <- bigrams %>% slice_max(n, n = 15)
ggplot(top_bigrams, aes(x = reorder(bigram, n), y = n)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = "Top Bigrams in Fake News Headlines",
x = "Bigrams", y = "Frequency") +
theme_minimal()
plot_ngrams <- function(df, title, top_n = 20) {
df %>%
as_tibble() %>%
arrange(desc(n)) %>%
head(top_n) %>%
ggplot(aes(x = reorder(!!sym(names(df)[1]), n), y = n)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = title, x = "N-Gram", y = "Frequency") +
theme_minimal()
}
plot_ngrams(trigrams, "Top 20 Trigrams")
sankey_data_trigram <- trigrams %>%
separate(trigram, into = c("word1", "word2", "word3"), sep = " ", remove = FALSE) %>%
top_n(20, wt = n)
links_trigram <- data.frame(
source = c(sankey_data_trigram$word1, sankey_data_trigram$word2),
target = c(sankey_data_trigram$word2, sankey_data_trigram$word3),
value = rep(sankey_data_trigram$n, 2)
)
nodes_trigram <- data.frame(name = unique(c(links_trigram$source, links_trigram$target)))
links_trigram$source <- match(links_trigram$source, nodes_trigram$name) - 1
links_trigram$target <- match(links_trigram$target, nodes_trigram$name) - 1
sankeyNetwork(Links = links_trigram, Nodes = nodes_trigram, Source = "source", Target = "target",
Value = "value", NodeID = "name", fontSize = 12, nodeWidth = 30)
plot_ngrams(quadgrams, "Top 20 Quadgrams")
sankey_data_quadgram <- quadgrams %>%
separate(quadgram, into = c("word1", "word2", "word3", "word4"), sep = " ", remove = FALSE) %>%
top_n(20, wt = n)
links_quadgram <- data.frame(
source = c(sankey_data_quadgram$word1, sankey_data_quadgram$word2, sankey_data_quadgram$word3),
target = c(sankey_data_quadgram$word2, sankey_data_quadgram$word3, sankey_data_quadgram$word4),
value = rep(sankey_data_quadgram$n, 3)
)
nodes_quadgram <- data.frame(name = unique(c(links_quadgram$source, links_quadgram$target)))
links_quadgram$source <- match(links_quadgram$source, nodes_quadgram$name) - 1
links_quadgram$target <- match(links_quadgram$target, nodes_quadgram$name) - 1
sankeyNetwork(Links = links_quadgram, Nodes = nodes_quadgram, Source = "source", Target = "target",
Value = "value", NodeID = "name", fontSize = 12, nodeWidth = 30)
filtered_ngrams <- pentagrams %>%
filter(n > 5) %>%
separate(pentagram, into = c("w1", "w2", "w3", "w4", "w5"), sep = " ", remove = FALSE)
graph <- graph_from_data_frame(filtered_ngrams, directed = FALSE)
## Warning: In `d`, `NA` elements were replaced with string "NA".
ggraph(graph, layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE) +
geom_node_point(color = "blue", size = 3) +
geom_node_text(aes(label = name), repel = TRUE, size = 3) +
theme_minimal()
hexagram_data <- hexagrams %>%
top_n(20, wt = n) %>%
separate(hexagram, into = c("w1", "w2", "w3", "w4", "w5", "w6"), sep = " ", remove = FALSE) %>%
mutate(category = paste(w1, w2, sep = "_"))
treemap(hexagram_data,
index = "hexagram",
vSize = "n",
vColor = "n",
type = "index",
palette = "Blues",
title = "Hexagram Treemap")
nrc_lexicon <- read_excel("~/Downloads/NRC-Emotion-Lexicon-v0.92-In105Languages-Nov2017Translations.xlsx")
## New names:
## • `English (en)` -> `English (en)...1`
## • `English (en)` -> `English (en)...22`
nrc_cleaned <- nrc_lexicon %>%
select(Word = `English (en)...1`,
Positive, Negative, Anger, Anticipation,
Disgust, Fear, Joy, Sadness, Surprise, Trust)
nrc_cleaned <- nrc_cleaned %>%
rename(word = Word)
headlines_emotions <- tidy_headlines %>%
inner_join(nrc_cleaned, by = "word") %>%
group_by(id = row_number()) %>%
summarize(across(c(Positive:Trust), sum, na.rm = TRUE))
headlines_emotions_df <- headlines_df %>%
mutate(id = row_number()) %>%
left_join(headlines_emotions, by = "id")
headlines_emotions_df %>%
summarize(across(Positive:Trust, mean, na.rm = TRUE))
## # A tibble: 1 × 10
## Positive Negative Anger Anticipation Disgust Fear Joy Sadness Surprise Trust
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.174 0.214 0.115 0.115 0.0775 0.156 0.0673 0.0920 0.181 0.139
headlines_emotions_df %>%
summarize(across(Positive:Trust, sum, na.rm = TRUE)) %>%
pivot_longer(cols = everything(), names_to = "Emotion", values_to = "Count") %>%
ggplot(aes(x = reorder(Emotion, -Count), y = Count, fill = Emotion)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(title = "Overall Emotional Distribution in Headlines",
x = "Emotion",
y = "Count") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
headlines_emotions_filtered <- headlines_emotions_df %>%
filter(rowSums(select(., Positive:Trust) > 0) > 0)
heatmap_data <- as.matrix(headlines_emotions_filtered %>% select(Positive:Trust))
heatmap(heatmap_data,
scale = "column",
col = heat.colors(256),
margins = c(5, 10),
xlab = "Emotion",
ylab = "Headline",
main = "Emotion Heatmap of Fake News Headlines")
base_url <- "https://thebridge.in/latest"
all_headlines <- c()
scrape_page <- function(url) {
page_html <- read_html(url)
h3_headlines <- page_html %>%
html_nodes("h3.bd-heading-text") %>%
html_text(trim = TRUE)
h4_headlines <- page_html %>%
html_nodes("h4.bd-heading-text") %>%
html_text(trim = TRUE)
all_headlines <- c(h3_headlines, h4_headlines)
return(all_headlines)
}
for (i in 1:1000) {
page_url <- ifelse(i == 1, base_url, paste0(base_url, "/", i))
cat("Scraping URL:", page_url, "\n")
headlines <- scrape_page(page_url)
all_headlines <- c(all_headlines, headlines)
cat("Scraped page", page_url, "with", length(headlines), "headlines.\n")
}
## Scraping URL: https://thebridge.in/latest
## Scraped page https://thebridge.in/latest with 12 headlines.
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bridge_headlines_df <- data.frame(headline = all_headlines)
process_headlines_full <- function(df, text_col, lexicon) {
library(tidyverse)
library(tidytext)
library(rvest)
library(wordcloud)
library(wordcloud2)
library(RColorBrewer)
library(igraph)
library(ggraph)
library(networkD3)
library(treemapify)
library(reshape2)
df <- df %>%
rename(title = {{ text_col }}) %>%
mutate(title = tolower(title))
tidy_df <- df %>%
unnest_tokens(word, title) %>%
anti_join(stop_words, by = "word") %>%
filter(nchar(word) > 1) %>%
mutate(word = gsub("[[:punct:]]", "", word))
word_freq <- tidy_df %>%
count(word, sort = TRUE)
bigrams <- df %>%
unnest_tokens(bigram, title, token = "ngrams", n = 2) %>%
count(bigram, sort = TRUE)
trigrams <- df %>%
unnest_tokens(trigram, title, token = "ngrams", n = 3) %>%
count(trigram, sort = TRUE)
quadgrams <- df %>%
unnest_tokens(quadgram, title, token = "ngrams", n = 4) %>%
count(quadgram, sort = TRUE)
pentagrams <- df %>%
unnest_tokens(pentagram, title, token = "ngrams", n = 5) %>%
count(pentagram, sort = TRUE)
hexagrams <- df %>%
unnest_tokens(hexagram, title, token = "ngrams", n = 6) %>%
count(hexagram, sort = TRUE)
wordcloud(words = word_freq$word,
freq = word_freq$n,
min.freq = 2,
max.words = 100,
random.order = FALSE,
rot.per = 0.3,
colors = brewer.pal(8, "Dark2"))
unigram_bar <- ggplot(word_freq %>% slice_max(n, n = 15),
aes(x = reorder(word, n), y = n)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = "Top Words in Headlines", x = "Words", y = "Frequency") +
theme_minimal()
wordcloud2::wordcloud2(bigrams %>% filter(n > 5), size = 0.5)
plot_ngrams <- function(ngram_df, title_text) {
ngram_df %>%
as_tibble() %>%
arrange(desc(n)) %>%
head(20) %>%
ggplot(aes(x = reorder(!!sym(names(ngram_df)[1]), n), y = n)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = title_text, x = "N-Gram", y = "Frequency") +
theme_minimal()
}
trigram_bar <- plot_ngrams(trigrams, "Top 20 Trigrams")
quadgram_bar <- plot_ngrams(quadgrams, "Top 20 Quadgrams")
create_sankey <- function(ngram_df, n_parts) {
splits <- paste0("word", 1:n_parts)
ngram_split <- ngram_df %>%
separate(col = !!sym(names(ngram_df)[1]), into = splits, sep = " ", remove = FALSE) %>%
filter(!is.na(.[[2]]))
links_list <- list()
for(i in 1:(n_parts-1)) {
links_list[[i]] <- ngram_split %>%
transmute(source = .[[splits[i]]],
target = .[[splits[i+1]]],
value = n)
}
links_df <- bind_rows(links_list)
nodes_df <- data.frame(name = unique(c(links_df$source, links_df$target)))
links_df <- links_df %>%
mutate(source = match(source, nodes_df$name) - 1,
target = match(target, nodes_df$name) - 1)
sankeyNetwork(Links = links_df, Nodes = nodes_df, Source = "source",
Target = "target", Value = "value", NodeID = "name",
fontSize = 12, nodeWidth = 30)
}
trigram_sankey <- create_sankey(trigrams, 3)
quadgram_sankey <- create_sankey(quadgrams, 4)
pentagram_graph <- function(penta_df) {
penta_split <- penta_df %>%
separate(pentagram, into = c("w1", "w2", "w3", "w4", "w5"), sep = " ", remove = FALSE) %>%
filter(!is.na(w2))
g <- graph_from_data_frame(penta_split, directed = FALSE)
ggraph(g, layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE) +
geom_node_point(color = "blue", size = 3) +
geom_node_text(aes(label = name), repel = TRUE, size = 3) +
theme_minimal()
}
pentagram_network <- pentagram_graph(pentagrams)
hexagram_treemap <- ggplot(hexagrams, aes(area = n, fill = n, label = hexagram)) +
geom_treemap() +
geom_treemap_text(colour = "white", place = "centre") +
scale_fill_viridis_c() +
labs(title = "Hexagram Treemap") +
theme_minimal()
emotions <- tidy_df %>%
inner_join(lexicon, by = "word") %>%
group_by(id = row_number()) %>%
summarize(across(Positive:Trust, sum, na.rm = TRUE))
df_emotions <- df %>%
mutate(id = row_number()) %>%
left_join(emotions, by = "id")
mean_emotions <- df_emotions %>%
summarize(across(Positive:Trust, mean, na.rm = TRUE))
emotion_bar <- ggplot(mean_emotions %>% pivot_longer(cols = everything(), names_to = "Emotion", values_to = "Mean"),
aes(x = reorder(Emotion, -Mean), y = Mean, fill = Emotion)) +
geom_bar(stat = "identity") +
theme_minimal() +
labs(title = "Mean Emotion Scores", x = "Emotion", y = "Mean Score") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
df_emotions_filtered <- df_emotions %>%
filter(rowSums(select(., Positive:Trust) != 0) > 0)
emotion_heat_data <- as.matrix(df_emotions_filtered %>% select(Positive:Trust))
heatmap(emotion_heat_data,
scale = "column",
col = heat.colors(256),
margins = c(5, 10),
xlab = "Emotion",
ylab = "Headline",
main = "Emotion Heatmap of Headlines")
return(list(
word_freq_table = word_freq,
bigrams_table = bigrams,
trigrams_table = trigrams,
quadgrams_table = quadgrams,
pentagrams_table = pentagrams,
hexagrams_table = hexagrams,
mean_emotion_table = mean_emotions,
unigram_bar_plot = unigram_bar,
trigram_bar_plot = trigram_bar,
quadgram_bar_plot = quadgram_bar,
trigram_sankey = trigram_sankey,
quadgram_sankey = quadgram_sankey,
pentagram_network = pentagram_network,
hexagram_treemap = hexagram_treemap,
emotion_bar_chart = emotion_bar
))
}
bridge_headlines_df <- read_csv("bridge_headlines.csv")
## Rows: 12000 Columns: 1
## ── Column specification ──────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): headline
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
bridge_results <- process_headlines_full(bridge_headlines_df, headline, nrc_cleaned)
## Links is a tbl_df. Converting to a plain data frame.
## Links is a tbl_df. Converting to a plain data frame.
head(bridge_results$word_freq_table,60)
## # A tibble: 60 × 2
## word n
## <chr> <int>
## 1 india 1798
## 2 indian 1553
## 3 world 1311
## 4 cup 1149
## 5 asian 1085
## 6 2024 1011
## 7 womens 911
## 8 games 898
## 9 team 869
## 10 fc 841
## # ℹ 50 more rows
head(bridge_results$bigrams_table)
## # A tibble: 6 × 2
## bigram n
## <chr> <int>
## 1 asian games 546
## 2 world cup 453
## 3 olympics 2024 272
## 4 set to 206
## 5 satwik chirag 198
## 6 in the 190
head(bridge_results$trigrams_table)
## # A tibble: 6 × 2
## trigram n
## <chr> <int>
## 1 asian games 2023 117
## 2 fih pro league 100
## 3 mumbai city fc 82
## 4 hockey world cup 70
## 5 asian champions trophy 66
## 6 t20 world cup 64
head(bridge_results$quadgrams_table)
## # A tibble: 6 × 2
## quadgram n
## <chr> <int>
## 1 women's t20 world cup 49
## 2 indian men's hockey team 48
## 3 indians in action on 41
## 4 indian women's hockey team 37
## 5 fih pro league india 35
## 6 khelo india para games 33
head(bridge_results$pentagrams_table)
## # A tibble: 6 × 2
## pentagram n
## <chr> <int>
## 1 2024 indians in action on 24
## 2 at asian games 2023 full 21
## 3 asian games 2023 full team 19
## 4 games 2023 full team schedule 19
## 5 indians in action on september 18
## 6 2023 indians in action on 17
head(bridge_results$hexagrams_table)
## # A tibble: 6 × 2
## hexagram n
## <chr> <int>
## 1 <NA> 38
## 2 asian games 2023 full team schedule 19
## 3 at asian games 2023 full team 19
## 4 asian games 2023 indians in action 17
## 5 games 2023 indians in action on 17
## 6 games 2023 full team schedule medal 14
bridge_results$mean_emotion_table
## # A tibble: 1 × 10
## Positive Negative Anger Anticipation Disgust Fear Joy Sadness Surprise
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.225 0.110 0.0608 0.118 0.0188 0.0746 0.0888 0.0455 0.0635
## # ℹ 1 more variable: Trust <dbl>
print(bridge_results$unigram_bar_plot)
print(bridge_results$trigram_bar_plot)
print(bridge_results$quadgram_bar_plot)
print(bridge_results$emotion_bar_chart)
top_connections <- bridge_results$trigram_sankey$x$links %>%
dplyr::arrange(desc(value)) %>%
head(100)
bridge_results$trigram_sankey$x$links <- top_connections
links <- bridge_results$trigram_sankey$x$links
unique_nodes <- unique(c(links$source, links$target))
node_names <- bridge_results$trigram_sankey$x$nodes$name
links$source_word <- node_names[links$source + 1]
links$target_word <- node_names[links$target + 1]
plot <- plot_ly(
type = "sankey",
node = list(
pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_names # Use words as node labels
),
link = list(
source = match(links$source_word, node_names) - 1, # Use the words' indices in node_names
target = match(links$target_word, node_names) - 1, # Use the words' indices in node_names
value = links$value
)
)
plot
top_quadgram_connections <- bridge_results$quadgram_sankey$x$links %>%
dplyr::arrange(desc(value)) %>%
head(100)
bridge_results$quadgram_sankey$x$links <- top_quadgram_connections
links_quadgram <- bridge_results$quadgram_sankey$x$links
node_names_quadgram <- bridge_results$quadgram_sankey$x$nodes$name
links_quadgram$source_word <- node_names_quadgram[links_quadgram$source + 1]
links_quadgram$target_word <- node_names_quadgram[links_quadgram$target + 1]
plot_quadgram <- plot_ly(
type = "sankey",
node = list(
pad = 15,
thickness = 20,
line = list(color = "black", width = 0.5),
label = node_names_quadgram
),
link = list(
source = match(links_quadgram$source_word, node_names_quadgram) - 1,
target = match(links_quadgram$target_word, node_names_quadgram) - 1,
value = links_quadgram$value
)
)
plot_quadgram